Imaging system and method for use in surgical and interventional medical procedures

ABSTRACT

A system and method for displaying images of internal anatomy includes an image processing device configured to provide high resolution images of the surgical field from low resolution scans during the procedure. The image processing device digitally manipulates a previously-obtained high resolution baseline image to produce many representative images based on permutations of movement of the baseline image. During the procedure a representative image is selected having an acceptable degree of correlation to the new low resolution image. The selected representative image and the new image are merged to provide a higher resolution image of the surgical field. The image processing device is also configured to provide interactive movement of the displayed image based on movement of the imaging device, and to permit placement of annotations on the displayed image to facilitate communication between the radiology technician and the surgeon.

CROSS-REFERENCE TO RELATED APPLICATION

This application claims priority to and is a continuation of U.S.application Ser. No. 16/558,155, filed Sep. 1, 2019, (currentlypending), which claims priority to and is a continuation of Ser. Ser.No. 16/200,519, filed Nov. 26, 2018, and issued on Oct. 15, 2019 as U.S.Pat. No. 10,444,855, which claims priority to and is a continuation ofU.S. application Ser. No. 15/713,265, filed Sep. 22, 2017, and issued onNov. 27, 2018 as U.S. Pat. No. 10,139,920, which claims priority to andis a continuation of U.S. application Ser. No. 14/564,728, filed Dec. 9,2014, and issued on Oct. 10, 2017 as U.S. Pat. No. 9,785,246, whichclaims priority to and is a continuation-in-part of U.S. applicationSer. No. 14/270,446, filed on May 6, 2014, and issued on Dec. 9, 2014 asU.S. Pat. No. 8,908,952, which is a continuation of U.S. application No.Ser. No. 13/722,259, filed on Dec. 20, 2012, and issued on May 6, 2014,as U.S. Pat. No. 8,718,346, which is a continuation-in-part of U.S.application Ser. No. 13/253,838, filed on Oct. 5, 2011, and issued onSep. 3, 2013, as U.S. Pat. No. 8,526,700, which was a non-provisional ofProvisional Application No. 61/390,488, filed on Oct. 6, 2010, theentire disclosures of which are incorporated herein by reference. U.S.application Ser. No. 14/564,728 is also a non-provisional of and furtherclaims priority to Provisional Application No. 62/036,660, filed on Aug.13, 2014, the entire disclosure of which is incorporated herein byreference.

BACKGROUND

The present invention contemplates a system and method for altering theway a patient image, such as by X-ray, is viewed and obtained. Moreparticularly, the inventive system and method provides means fordecreasing the overall radiation to which a patient is exposed during asurgical procedure but without significantly sacrificing the quality orresolution of the image obtained.

Many surgical procedures require obtaining an image of the patient'sinternal body structure, such as organs and bones. In some procedures,the surgery is accomplished with the assistance of periodic images ofthe surgical site. Surgery can broadly mean any invasive testing orintervention performed by medical personnel, such as surgeons,interventional radiologists, cardiologists, pain management physicians,and the like. In surgeries and interventions that are in effect guidedby serial imaging, which we will refer to as image guided, frequentpatient images are necessary for the physician's proper placement ofsurgical instruments, be they catheters, needles, instruments orimplants, or performance of certain medical procedures. Fluoroscopy, orfluoro, is one form of intraoperative X-ray and is taken by a fluorounit, also known as a C-arm. The C-arm sends X-ray beams through apatient and takes a picture of the anatomy in that area, such asskeletal and vascular structure. It is, like any picture, atwo-dimensional (2D) image of a three-dimensional (3D) space. However,like any picture taken with a camera, key 3D info may be present in the2D image based on what is in front of what and how big one thing isrelative to another.

A digitally reconstructed radiograph (DRR) is a digital representationof an X-ray made by taking a CT scan of a patient and simulating takingX-rays from different angles and distances. The result is that anypossible X-ray that can be taken for that patient can be simulated,which is unique and specific to how the patient's anatomical featureslook relative to one another. Because the “scene” is controlled, namelyby controlling the virtual location of a C-Arm to the patient and theangle relative to one another, a picture can be generated that shouldlook like any X-ray taken in the operating room (OR).

Many imaging approaches, such as taking fluoro images, involve exposingthe patient to radiation, albeit in small doses. However, in these imageguided procedures, the number of small doses adds up so that the totalradiation exposure can be problematic not only to the patient but alsoto the surgeon or radiologist and others participating in the surgicalprocedure. There are various known ways to decrease the amount ofradiation exposure for a patient/surgeon when an image is taken, butthese approaches come at the cost of decreasing the resolution of theimage being obtained. For example, certain approaches use pulsed imagingas opposed to standard imaging, while other approaches involve manuallyaltering the exposure time or intensity. Narrowing the field of view canpotentially also decrease the area of radiation exposure and itsquantity (as well as alter the amount of radiation “scatter”) but againat the cost of lessening the information available to the surgeon whenmaking a medical decision. Collimators are available that can speciallyreduce the area of exposure to a selectable region. For instance, acollimator, such as the Model Series CM-1000 of Heustis Medical, isplaced in front of an x-ray source, such as the source 104 shown inFIG. 1. The collimator consists of a series of plates that absorb mostincident X-rays, such as lead. The only x-rays that reach the patientare those that pass through apertures between the plates. The positionof the plates can be controlled manually or automatically, and theplates may be configured to provide differently shaped fields, such amulti-sided field. Since the collimator specifically excludes certainareas of the patient from exposure to x-rays, no image is available inthose areas. The medical personnel thus have an incomplete view of thepatient, limited to the specifically selected area. Thus, while the useof a collimator reduces the radiation exposure to the patient, it comesat a cost of reducing the amount of information available to the medicalpersonnel.

Further, often times images taken during a surgical intervention areblocked either by extraneous OR equipment or the actualinstruments/implants used to perform the intervention. Limiting theblocking of the normal anatomy behind those objects would have tangiblebenefits to the medical community.

There is a need for a an imaging system, that can be used in connectionwith standard medical procedures, that reduces the radiation exposure tothe patient and medical personnel, but without any sacrifice in accuracyand resolution of an X-ray image. There is also a need for an imagingsystem that accounts for instruments and hardware, such as implants,that might otherwise obscure a full view of the surgical site.

SUMMARY

According to one aspect, a system and method is providing for generatinga display of a patient's internal anatomy for use in a surgical orinterventional medical procedure based on a previously acquired highresolution baseline image and a newly acquired low resolution image. Thehigh resolution image may be an image obtained during the procedure or apre-procedure image such as a DRR. The low resolution image may beacquired using a pulse and/or low dose radiation setting. The systemcontemplates an image processing device configured to digitallymanipulate the high resolution baseline image to produce a baselineimage set including representative images of the baseline image at aplurality of permutations of movements of the baseline image in 4D or 6Dspace. The new low resolution image is compared to the baseline imageset to select a representative image having an acceptable degree ofcorrelation with the new image. The image processing device mayimplement algorithms to perform the comparison, such as a principalcomponent analysis or other statistical test. The image processingdevice is further configured to merge the selected representative highresolution image with the new low resolution image to generate a mergedimage to be displayed. The merged image may be further processed toallow alternating between the selected high resolution image and the newlow resolution image, or to adjust the amount that the two images aremerged in the displayed image.

In another feature of the present disclosure, an imaging system mayinclude an image processing device that acts as a viewfinder as theimaging device is moved relative to the patient. In accordance with thisfeature, an image of the surgical field is acquired with the imagingdevice in a first orientation. That acquired image is continuouslydisplayed while the imaging device, patient or patient table is movedfrom the first orientation. This movement is tracked is used the imageprocessing device to move the displayed image in relation to the trackedmovement. With this feature, the display acts as a viewfinder to predicthow a new image would appear if captured at that time by the imagingdevice. This feature can thus be used to determine where the next liveimage of the patient's anatomy will be taken or can be used to assist institching multiple images together to form a larger panoramic view ofthe surgical field. The image processing system may implement softwareadapted to optimize the predicted image and minimize misalignment or offangle appearance of the display. In another aspect, the image processingsystem permits annotation of the displayed image to identify anatomicfeatures or desired image trajectories or alignments.

In a further feature of the disclosed embodiments, a baseline image ofanatomy within a surgical field is acquired in a baseline orientation,and that baseline image is digitally manipulated to produce a baselineimage set including representative images of the baseline image at aplurality of permutations of movements of the baseline image. A newimage of the surgical field in which portions of the anatomy are blockedby objects. This new image is compared to the baseline image set toselect a representative image having an acceptable degree of correlationwith the new image. The image processing system generates a displayedimage showing the surgical field with the blocking objects minimized oreliminated. The system further permits fading the blocked objects in andout of the display.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a pictorial view of an image guided surgical setting includingan imaging system and an image processing device, as well as a trackingdevice.

FIG. 2A is an image of a surgical field acquired using a full dose ofradiation in the imaging system.

FIG. 2B is an image of the surgical field shown in FIG. 2A in which theimage was acquired using a lower dose of radiation.

FIG. 2C is a merged image of the surgical field with the two imagesshown in FIGS. 2A-B merged in accordance with one aspect of the presentdisclosure.

FIG. 3 is a flowchart of graphics processing steps undertaken by theimage processing device shown in FIG. 1.

FIG. 4A is an image of a surgical field including an object blocking aportion of the anatomy.

FIG. 4B is an image of the surgical field shown in FIG. 4A with edgeenhancement.

FIG. 4C, FIG. 4D, FIG. 4E, FIG. 4F, FIG. 4G, FIG. 4H, FIG. 4I, and FIG.4J are images showing the surgical field of FIG. 4B with differentfunctions applied to determine the anatomic and non-anatomic features inthe view.

FIG. 4K and FIG. 4L are images of a mask generated using a threshold anda table lookup.

FIG. 4M and FIG. 4N are images of the masks shown in FIG. 4K and FIG.4L, respectively, after dilation and erosion.

FIG. 4O and FIG. 4P are images prepared by applying the masks of FIG. 4Mand FIG. 4N, respectively, to the filter image of FIG. 4B to eliminatethe non-anatomic features from the image.

FIG. 5A is an image of a surgical field including an object blocking aportion of the anatomy.

FIG. 5B is an image of the surgical field shown in FIG. 5A with theimage of FIG. 5A partially merged with a baseline image to display theblocked anatomy.

FIG. 6A and FIG. 6B are baseline and merged images of a surgical fieldincluding a blocking object.

FIG. 7A and FIG. 7B are displays of the surgical field adjusted formovement of the imaging device or C-arm and providing an indicator of anin-bounds or out-of-bounds position of the imaging device for acquiringa new image.

FIG. 8A and FIG. 8B are displays of the surgical field adjusted formovement of the imaging device or C-arm and providing an indicator ofwhen a new image can be stitched to a previously acquired image.

FIG. 8C is a screen print of a display showing a baseline image with atracking circle and direction of movement indicator for use in orientingthe C-arm for acquiring a new image.

FIG. 8D is a screen shot of a display of a two view finder used toassist in orienting the imaging device or C-arm to obtain a new image atthe same spatial orientation as a baseline image.

FIG. 9A and FIG. 9B are displays of the surgical field adjusted formovement of the imaging device or C-arm and providing an indicator ofalignment of the imaging device with a desired trajectory for acquiringa new image.

FIG. 10 is a depiction of a display and user interface for the imageprocessing device shown in FIG. 1.

FIG. 11 is a graphical representation of an image alignment processaccording to the present disclosure.

FIG. 12A is an image of a surgical field obtained through a collimator.

FIG. 12B is an image of the surgical field shown in FIG. 12A as enhancedby the systems and methods disclosed herein.

FIG. 13A, FIG. 13B, FIG. 14A, FIG. 14B, FIG. 15A, FIG. 15B, FIG. 16A andFIG. 16B are images showing a surgical field obtained through acollimator in which the collimator is moved

DETAILED DESCRIPTION

For the purposes of promoting an understanding of the principles of theinvention, reference will now be made to the embodiments illustrated inthe drawings and described in the following written specification. It isunderstood that no limitation to the scope of the invention is therebyintended. It is further understood that the present invention includesany alterations and modifications to the illustrated embodiments andincludes further applications of the principles of the invention aswould normally occur to one skilled in the art to which this inventionpertains.

A typical imaging system 100 is shown in FIG. 1. The imaging systemincludes a base unit 102 supporting a C-arm imaging device 103. TheC-arm includes a radiation source 104 that is positioned beneath thepatient P and that directs a radiation beam upward to the receiver 105.It is known that the radiation beam emanated from the source 104 isconical so that the field of exposure may be varied by moving the sourcecloser to or away from the patient. The source 104 may include acollimator that is configured to restrict the field of exposure. TheC-arm 103 may be rotated about the patient P in the direction of thearrow 108 for different viewing angles of the surgical site. In someinstances, implants or instruments T may be situated at the surgicalsite, necessitating a change in viewing angle for an unobstructed viewof the site. Thus, the position of the receiver relative to the patient,and more particularly relative to the surgical site of interest, maychange during a procedure as needed by the surgeon or radiologist.Consequently, the receiver 105 may include a tracking target 106 mountedthereto that allows tracking of the position of the C-arm using atracking device 130. For instance, the tracking target 106 may includeseveral infrared emitters spaced around the target, while the trackingdevice is configured to triangulate the position of the receiver 105from the infrared signals emitted by the element. The base unit 102includes a control panel 110 through which a radiology technician cancontrol the location of the C-arm, as well as the radiation exposure. Atypical control panel 110 thus permits the technician to “shoot apicture” of the surgical site at the surgeon's direction, control theradiation dose, and initiate a radiation pulse image.

The receiver 105 of the C-arm 103 transmits image data to an imageprocessing device 122. The image processing device can include a digitalmemory associated therewith and a processor for executing digital andsoftware instructions. The image processing device may also incorporatea frame grabber that uses frame grabber technology to create a digitalimage for projection as displays 123, 124 on a display device 126. Thedisplays are positioned for interactive viewing by the surgeon duringthe procedure. The two displays may be used to show a images from twoviews, such as lateral and AP, or may show a baseline scan and a currentscan of the surgical site, or a current scan and a “merged” scan basedon a prior baseline scan and a low radiation current scan, as describedherein. An input device 125, such as a keyboard or a touch screen, canallow the surgeon to select and manipulate the on-screen images. It isunderstood that the input device may incorporate an array of keys ortouch screen icons corresponding to the various tasks and featuresimplemented by the image processing device 122. The image processingdevice includes a processor that converts the image data obtained fromthe receiver 105 into a digital format. In some cases the C-arm may beoperating in the cinematic exposure mode and generating many images eachsecond. In these cases, multiple images can be averaged together over ashort time period into a single image to reduce motion artifacts andnoise.

In one aspect of the present invention, the image processing device 122is configured to provide high quality real-time images on the displays123, 124 that are derived from lower detail images obtained using lowerdoses (LD) of radiation. By way of example, FIG. 2A is a “full dose”(FD) x-ray image, while FIG. 2B is a low dose and/or pulsed (LD) imageof the same anatomy. It is apparent that the LD image is too “noisy” anddoes not provide enough information about the local anatomy for accurateimage guided surgery. While the FD image provides a crisp view of thesurgical site, the higher radiation dose makes taking multiple FD imagesduring a procedure highly problematic. Using the steps described herein,the surgeon is provided with a current image shown in FIG. 2C thatsignificantly reduces the noise of the LD image, in some cases by about90%, so that surgeon is provided with a clear real-time image using apulsed or low dose radiation setting. This capability allows fordramatically less radiation exposure during the imaging to verify theposition of instruments and implants during the procedure.

The flowchart of FIG. 3 depicts one embodiment of method according tothe present invention. In a first step 200, a baseline high resolutionFD image is acquired of the surgical site and stored in a memoryassociated with the image processing device. In some cases where theC-arm is moved during the procedure, multiple high resolution images canbe obtained at different locations in the surgical site, and then thesemultiple images “stitched” together to form a composite base image usingknown image stitching techniques). Movement of the C-arm, and moreparticularly “tracking” the acquired image during these movements, isaccounted for in other steps described in more detail herein. For thepresent discussion it is assumed that the imaging system is relativefixed, meaning that only very limited movement of the C-arm and/orpatient are contemplated, such as might arise in an epidural painprocedure, spinal K-wire placement or stone extraction. The baselineimage is projected in step 202 on the display 123 for verification thatthe surgical site is properly centered within the image. In some cases,new FD images may be obtained until a suitable baseline image isobtained. In procedures in which the C-arm is moved, new baseline imagesare obtained at the new location of the imaging device, as discussedbelow. If the displayed image is acceptable as a baseline image, abutton may be depressed on a user interface, such as on the displaydevice 126 or interface 125. In procedures performed on anatomicalregions where a substantial amount of motion due to physiologicalprocesses (such as respiration) is expected, multiple baseline imagesmay be acquired for the same region over multiple phases of the cycle.These images may be tagged to temporal data from other medicalinstruments, such as an ECG or pulse oximeter.

Once the baseline image is acquired, a baseline image set is generatedin step 204 in which the original baseline image is digitally rotated,translated and resized to create thousands of permutations of theoriginal baseline image. For instance, a typical two dimensional (2D)image of 128×128 pixels may be translated ±15 pixels in the x and ydirections at 1 pixel intervals, rotated ±9° at 3° intervals and scaledfrom 92.5% to 107.5% at 2.5% intervals (4 degrees of freedom, 4D),yielding 47,089 images in the baseline image set. (A three-dimensional(3D) image will imply a 6D solution space due to the addition of twoadditional rotations orthogonal to the x and y axis. An original CTimage data set can be used to form many thousands of DRRs in a similarfashion.) Thus, in this step, the original baseline image spawnsthousands of new image representations as if the original baseline imagewas acquired at each of the different movement permutations. This“solution space” may be stored in a graphics card memory, such as in thegraphics processing unit (GPU) of the image processing device 122, instep 206 or formed as a new image which is then sent to the GPU,depending on the number of images in the solution space and the speed atwhich the GPU can produce those images. With current computing power, ona free standing, medical grade computer, the generation of a baselineimage set having nearly 850,000 images can occur in less than one secondin a GPU because the multiple processors of the GPU can eachsimultaneously process an image.

During the procedure, a new LD image is acquired in step 208, stored inthe memory associated with the image processing device, and projected ondisplay 123. Since the new image is obtained at a lower dose ofradiation it is very noisy. The present invention thus provides stepsfor “merging” the new image with an image from the baseline image set toproduce a clearer image on the second display 124 that conveys moreuseful information to the surgeon. The invention thus contemplates animage recognition or registration step 210 in which the new image iscompared to the images in the baseline image set to find a statisticallymeaningful match. A new “merged” image is generated in step 212 that maybe displayed on display 124 adjacent the view of the original new image.At various times throughout the procedure, a new baseline image may beobtained in step 216 that is used to generate a new baseline image setin step 204.

Step 210 contemplates comparing the current new image to the images inthe baseline image set. Since this step occurs during the surgicalprocedure, time and accuracy are critical. Preferably, the step canobtain an image registration in less than one second so that there is nomeaningful delay between when the image is taken by the C-arm and whenthe merged image is displayed on the device 126. Various algorithms maybe employed that may be dependent on various factors, such as the numberof images in the baseline image set, the size and speed of the computerprocessor or graphics processor performing the algorithm calculations,the time allotted to perform the computations, and the size of theimages being compared (e.g., 128×128 pixels, 1024×1024 pixels, etc). Inone approach, comparisons are made between pixels at predeterminedlocations described above in a grid pattern throughout 4D space. Inanother heuristic approach, pixel comparisons can be concentrated inregions of the images believed to provide a greater likelihood of arelevant match. These regions may be “pre-seeded” based on knowledgefrom a grid or PCA search (defined below), data from a tracking system(such as an optical surgical navigation device), or location data fromthe DICOM file or the equivalent. Alternatively, the user can specifyone or more regions of the image for comparison by marking on thebaseline image the anatomical features considered to be relevant to theprocedure. With this input each pixel in the region can be assigned arelevance score between 0 and 1 which scales the pixel's contribution tothe image similarity function when a new image is compared to thebaseline image. The relevance score may be calibrated to identifyregion(s) to be concentrated on or region(s) to be ignored.

In another approach, a principal component analysis (PCA) is performed,which can allow for comparison to a larger number of larger images inthe allotted amount of time than is permitted with the full resolutiongrid approach. In the PCA approach, a determination is made as to howeach pixel of the image set co-varies with each other. A covariancematrix may be generated using only a small portion of the total solutionset—for instance, a randomly selected 10% of the baseline image set.Each image from the baseline image set is converted to a column vector.In one example, a 70×40 pixel image becomes a 2800×1 vector. Thesecolumn vectors are normalized to a mean of 0 and a variance of 1 andcombined into a larger matrix. The covariance matrix is determined fromthis larger matrix and the largest eigenvectors are selected. For thisparticular example, it has been found that 30 PCA vectors can explainabout 80% of the variance of the respective images. Thus, each 2800×1image vector can be multiplied by a 2800×30 PCA vector to yield a 1×30vector. The same steps are applied to the new image—the new image isconverted to a 2800×1 image vector and multiplication with the 2800×30PCA vector produces a 1×30 vector corresponding to the new image. Thesolution set (baseline image) vectors and the new image vector arenormalized and the dot product of the new image vector to each vector inthe solution space is calculated. The solution space baseline imagevector that yields the largest dot product (i.e., closest to 1) isdetermined to be the closest image to the new image. It is understoodthat the present example may be altered with different image sizesand/or different principal components used for the analysis. It isfurther understood that other known techniques may be implemented thatmay utilize eigenvectors, singular value determination, mean squarederror, mean absolute error, and edge detection, for instance. It isfurther contemplated that various image recognition approaches can beapplied to selected regions of the images or that various statisticalmeasures may be applied to find matches falling within a suitableconfidence threshold. A confidence or correlation value may be assignedthat quantifies the degree of correlation between the new image and theselected baseline image, or selected ones of the baseline image set, andthis confidence value may be displayed for the surgeon's review. Thesurgeon can decide whether the confidence value is acceptable for theparticular display and whether another image should be acquired.

In the image guided surgical procedures, tools, implants and instrumentswill inevitably appear in the image field. These objects are typicallyradiodense and consequently block the relevant patient anatomy fromview. The new image obtained in step 210 will thus include an artifactof the tool T that will not correlate to any of the baseline image set.The presence of the tool in the image thus ensures that the comparisontechniques described above will not produce a high degree ofregistration between the new image and any of the baseline image set.Nevertheless, if the end result of each of the above procedures is toseek out the highest degree of correlation, which is statisticallyrelevant or which exceeds a certain threshold, the image registrationmay be conducted with the entire new image, tool artifact and all.

Alternatively, the image registration steps may be modified to accountfor the tool artifacts on the new image. In one approach, the new imagemay be evaluated to determine the number of image pixels that are“blocked” by the tool. This evaluation can involve comparing a grayscalevalue for each pixel to a threshold and excluding pixels that falloutside that threshold. For instance, if the pixel grayscale values varyfrom 0 (completely blocked) to 10 (completely transparent), a thresholdof 3 may be applied to eliminate certain pixels from evaluation.Additionally, when location data is available for various tracked tools,algorithmically areas that are blocked can be mathematically avoided.

In another approach, the image recognition or registration step 210 mayinclude steps to measure the similarity of the LD image to a transformedversion of the baseline image (i.e., a baseline image that has beentransformed to account for movement of the C-arm, as described belowrelative to FIG. 11) or of the patient. In an image-guided surgicalprocedure, the C-arm system acquires multiple X-ray images of the sameanatomy. Over the course of this series of images the system may move insmall increments and surgical tools may be added or removed from thefield of view, even though the anatomical features may remain relativelystable. The approach described below takes advantage of this consistencyin the anatomical features by using the anatomical features present inone image to fill in the missing details in another later image. Thisapproach further allows the transfer of the high quality of a full doseimage to subsequent low dose images.

In the present approach, a similarity function in the form of a scalarfunction of the images is used to determine the registration between acurrent LD image and a baseline image. To determine this registration itis first necessary to determine the incremental motion that has occurredbetween images. This motion can be described by four numberscorresponding to four degrees of freedom—scale, rotation and verticaland horizontal translation. For a given pair of images to be comparedknowledge of these four numbers allows one of the images to bemanipulated so that the same anatomical features appear in the samelocation between both images. The scalar function is a measure of thisregistration and may be obtained using a correlation coefficient, dotproduct or mean square error. By way of example, the dot product scalarfunction corresponds to the sum of the products of the intensity valuesat each pixel pair in the two images. For example, the intensity valuesfor the pixel located at 1234, 1234 in each of the LD and baselineimages are multiplied. A similar calculation is made for every otherpixel location and all of those multiplied values are added for thescalar function. It can be appreciated that when two images are in exactregistration this dot product will have the maximum possible magnitude.In other words, when the best combination is found, the correspondingdot product it typically higher than the others, which may be reportedas the Z score (i.e., number of standard deviations above the mean). A Zscore greater than 7.5 represents a 99.9999999% certainty that theregistration was not found by chance. It should be borne in mind thatthe registration being sought using this dot product is between abaseline image of a patient's anatomy and a real-time low dose image ofthat same anatomy taken at a later time after the viewing field andimaging equipment may have moved or non-anatomical objects introducedinto the viewing field.

This approach is particularly suited to performance using a parallelcomputing architecture such as the GPU which consists of multipleprocessors capable of performing the same computation in parallel. Eachprocessor of the GPU may thus be used to compute the similarity functionof the LD image and one transformed version of the baseline image. Inthis way, multiple transformed versions of the baseline image can becompared to the LD image simultaneously. The transformed baseline imagescan be generated in advance when the baseline is acquired and thenstored in GPU memory. Alternatively, a single baseline image can bestored and transformed on the fly during the comparison by reading fromtransformed coordinates with texture fetching. In situations in whichthe number of processors of the GPU greatly exceeds the number oftransformations to be considered, the baseline image and the LD imagecan be broken into different sections and the similarity functions foreach section can be computed on different processors and thensubsequently merged.

To further accelerate the determination of the best transformation toalign two images, the similarity functions can first be computed withdown-sampled images that contain fewer pixels. This down-sampling can beperformed in advance by averaging together groups of neighboring pixels.The similarity functions for many transformations over a broad range ofpossible motions can be computed for the down-sampled images first. Oncethe best transformation from this set is determined that transformationcan be used as the center for a finer grid of possible transformationsapplied to images with more pixels. In this way, multiple steps are usedto determine the best transformation with high precision whileconsidering a wide range of possible transformations in a short amountof time.

In order to reduce the bias to the similarity function caused bydifferences in the overall intensity levels in the different images, andto preferentially align anatomical features in the images that are ofinterest to the user, the images can be filtered before the similarityfunction is computed. Such filters will ideally suppress the very highspatial frequency noise associated with low dose images, while alsosuppressing the low spatial frequency information associated with large,flat regions that lack important anatomical details. This imagefiltration can be accomplished with convolution, multiplication in theFourier domain or Butterworth filters, for example. It is thuscontemplated that both the LD image and the baseline image(s) will befiltered accordingly prior to generating the similarity function.

As previously explained, non-anatomical features may be present in theimage, such as surgical tools, in which case modifications to thesimilarity function computation process may be necessary to ensure thatonly anatomical features are used to determine the alignment between LDand baseline images. A mask image can be generated that identifieswhether or not a pixel is part of an anatomical feature. In one aspect,an anatomical pixel may be assigned a value of 1 while a non-anatomicalpixel is assigned a value of 0. This assignment of values allows boththe baseline image and the LD image to be multiplied by thecorresponding mask images before the similarity function is computed asdescribed above In other words, the mask image can eliminate thenon-anatomical pixels to avoid any impact on the similarity functioncalculations.

To determine whether or not a pixel is anatomical, a variety offunctions can be calculated in the neighborhood around each pixel. Thesefunctions of the neighborhood may include the standard deviation, themagnitude of the gradient, and/or the corresponding values of the pixelin the original grayscale image and in the filtered image. The“neighborhood” around a pixel includes a pre-determined number ofadjacent pixels, such as a 5×5 or a 3×3 grid. Additionally, thesefunctions can be compounded, for example, by finding the standarddeviation of the neighborhood of the standard deviations, or bycomputing a quadratic function of the standard deviation and themagnitude of the gradient. One example of a suitable function of theneighborhood is the use of edge detection techniques to distinguishbetween bone and metallic instruments. Metal presents a “sharper” edgethan bone and this difference can be determined using standard deviationor gradient calculations in the neighborhood of an “edge” pixel. Theneighborhood functions may thus determine whether a pixel is anatomic ornon-anatomic based on this edge detection approach and assign a value of1 or 0 as appropriate to the pixel.

Once a set of values has been computed for the particular pixel, thevalues can be compared against thresholds determined from measurementsof previously-acquired images and a binary value can be assigned to thepixel based on the number of thresholds that are exceeded.Alternatively, a fractional value between 0 and 1 may be assigned to thepixel, reflecting a degree of certainty about the identity of the pixelas part of an anatomic or non-anatomic feature. These steps can beaccelerated with a GPU by assigning the computations at one pixel in theimage to one processor on the GPU, thereby enabling values for multiplepixels to be computed simultaneously. The masks can be manipulated tofill in and expand regions that correspond to non-anatomical featuresusing combinations of morphological image operations such as erosion anddilation.

An example of the steps of this approach is illustrated in the images ofFIGS. 4A-4P. In FIG. 4A, an image of a surgical site includes anatomicfeatures (the patient's skull) and non-anatomic features (such as aclamp). The image of FIG. 4A is filtered for edge enhancement to producethe filtered image of FIG. 4B. It can be appreciated that this image isrepresented by thousands of pixels in a conventional manner, with theintensity value of each pixel modified according to the edge enhancementattributes of the filter. In this example, the filter is a Butterworthfilter. This filtered image is then subject to eight differenttechniques for generating a mask corresponding to the non-anatomicfeatures. Thus, the neighborhood functions described above (namely,standard deviation, gradient and compounded functions thereof) areapplied to the filtered image FIG. 4B to produce different images FIGS.4C-4J. Each of these images is stored as a baseline image for comparisonto and registration with a live LD image.

Thus, each image of FIGS. 4C-4J is used to generate a mask. As explainedabove, the mask generation process may be by comparison of the pixelintensities to a threshold value or by a lookup table in which intensityvalues corresponding to known non-anatomic features is compared to thepixel intensity. The masks generated by the threshold and lookup tabletechniques for one of the neighborhood function images is shown in FIGS.4K-4L. The masks can then be manipulated to fill in and expand regionsthat correspond to the non-anatomical features, as represented in theimages of FIGS. 4M-4N. The resulting mask is then applied to thefiltered image of FIG. 4B to produce the “final” baseline images ofFIGS. 4O-4P that will be compared to the live LD image. As explainedabove, each of these calculations and pixel evaluations can be performedin the individual processors of the GPU so that all of these images canbe generated in an extremely short time. Moreover, each of these maskedbaseline images can be transformed to account for movement of thesurgical field or imaging device and compared to the live LD image tofind the baseline image that yields the highest Z score corresponding tothe best alignment between baseline and LD images. This selectedbaseline image is then used in manner explained below.

Once the image registration is complete, the new image may be displayedwith the selected image from the baseline image set in different ways.In one approach, the two images are merged, as illustrated in FIGS. 5A,5B. The original new image is shown in FIG. 5A with the instrument Tplainly visible and blocking the underlying anatomy. A partially mergedimage generated in step 212 (FIG. 3) is shown in FIG. 5B in which theinstrument T is still visible but substantially mitigated and theunderlying anatomy is visible. The two images may be merged by combiningthe digital representation of the images in a conventional manner, suchas by adding or averaging pixel data for the two images. In oneembodiment, the surgeon may identify one or more specific regions ofinterest in the displayed image, such as through the user interface 125,and the merging operation can be configured to utilize the baselineimage data for the display outside the region of interest and conductthe merging operation for the display within the region of interest. Theuser interface 125 may be provided with a “slider” that controls theamount the baseline image versus the new image that is displayed in themerged image. In another approach, the surgeon may alternate between thecorrelated baseline image and the new image or merged image, as shown inFIGS. 6A, 6B. The image in FIG. 6A is the image from the baseline imageset found to have the highest degree of correlation to the new image.The image in FIG. 6B is the new image obtained. The surgeon mayalternate between these views to get a clearer view of the underlyinganatomy and a view of the current field with the instrumentation T,which in effect by alternating images digitally removes the instrumentfrom the field of view, clarifying its location relative to the anatomyblocked by it.

In another approach, a logarithmic subtraction can be performed betweenthe baseline image and the new image to identify the differences betweenthe two images. The resulting difference image (which may contain toolsor injected contrast agent that are of interest to the surgeon) can bedisplayed separately, overlaid in color or added to the baseline image,the new image or the merged image so that the features of interestappear more obvious. This may require the image intensity values to bescaled prior to subtraction to account for variations in the C-armexposure settings. Digital image processing operations such as erosionand dilation can be used to remove features in the difference image thatcorrespond to image noise rather than physical objects. The approach maybe used to enhance the image differences, as described, or to remove thedifference image from the merged image. In other words, the differenceimage may be used as a tool for exclusion or inclusion of the differenceimage in the baseline, new or merged images.

As described above, the image enhancement system of the presentdisclosure can be used to minimize radio-opaque instruments and allowvisualization of anatomy underlying the instrumentation. Alternatively,the present system can be operable to enhance selected instrumentationin an image or collection of images. In particular, the masks describeabove used to identify the location of the non-anatomic features can beselectively enhanced in an image. The same data can also be alternatelymanipulated to enhance the anatomic features and the selectedinstrumentation. This feature can be used to allow the surgeon toconfirm that the visualized landscape looks as expected, to helpidentify possible distortions in the image, and to assist in imageguided instrumentation procedures. Since the bone screw is radio-opaqueit can be easily visualized under a very low dose x-ray a low dose newimage can be used to identify the location of the instrumentation whilemerged with the high dose baseline anatomy image. Multiple very low doseimages can be acquired as the bone screw is advanced into the bone toverify the proper positioning of the bone screw. Since the geometry ofthe instrument, such as the bone screw, is known (or can be obtained orderived such as from image guidance, 2-D projection or both), the pixeldata used to represent the instrument in the x-ray image can be replacedwith a CAD model mapped onto the edge enhanced image of the instrument.

As indicated above, the present invention also contemplates a surgicalnavigation procedure in which the imaging device or C-arm 103 is moved.Thus, the present invention contemplates tracking the position of theC-arm rather than tracking the position of the surgical instruments andimplants as in traditional surgical navigation techniques, usingcommercially available tracking devices or the DICOM information fromthe imaging device. Tracking the C-arm requires a degree of accuracythat is much less than the accuracy required to track the instrumentsand implants. In this embodiment, the image processing device 122receives tracking information from the tracking device 130. The objectof this aspect of the invention is to ensure that the surgeon sees animage that is consistent with the actual surgical site regardless of theorientation of the C-arm relative to the patient.

Tracking the position of the C-arm can account for “drift”, which is agradual misalignment of the physical space and the imaging (or virtual)space. This “drift” can occur because of subtle patient movements,inadvertent contact with the table or imaging device and even gravity.This misalignment is often visually imperceptible, but can generatenoticeable shifts in the image viewed by the surgeon. These shifts canbe problematic when the surgical navigation procedure is being performed(and a physician is relying on the information obtained from thisdevice) or when alignment of new to baseline images is required toimprove image clarity. The use of image processing eliminates theinevitable misalignment of baseline and new images. The image processingdevice 122 further may incorporate a calibration mode in which thecurrent image of the anatomy is compared to the predicted image. Thedifference between the predicted and actual movement of the image can beaccounted for by an inaccurate knowledge of the “center of mass” or COM,described below, and drift. Once a few images are obtained and the COMis accurately established, recalibration of the system can occurautomatically with each successive image taken and thereby eliminatingthe impact of drift.

The image processing device 122 may operate in a “tracking mode” inwhich the movement of the C-arm is monitored and the currently displayedimage is moved accordingly. The currently displayed image may be themost recent baseline image, a new LD image or a merged image generatedas described above. This image remains on one of the displays 123, 124until a new picture is taken by the imaging device 100. This image isshifted on the display to match the movement of the C-arm using theposition data acquired by the tracking device 130. A tracking circle 240may be shown on the display, as depicted in FIGS. 7A, 7B. The trackingcircle identifies an “in bounds” location for the image. When thetracking circle appears in red, the image that would be obtained withthe current C-arm position would be “out of bounds” in relation to abaseline image position, as shown in FIG. 7A. As the C-arm is moved bythe radiology technician the representative image on the display ismoved. When the image moves “in bounds”, as shown in FIG. 7B, thetracking circle 240 turns green so that the technician has an immediateindication that the C-arm is now in a proper position for obtaining anew image. The tracking circle may be used by the technician to guidethe movements of the C-arm during the surgical procedure. The trackingcircle may also be used to assist the technician in preparing a baselinestitched image. Thus, an image position that is not properly aligned forstitching to another image, as depicted in FIG. 8A, will have a redtracking circle 240, while a properly aligned image position, as shownin FIG. 8B, will have a green tracking circle. The technician can thenacquire the image to form part of the baseline stitched image.

The tracking circle 240 may include indicia on the circumference of thecircle indicative of the roll position of the C-arm in the baselineimage. A second indicia, such as an arrow, may also be displayed on thecircumference of the tracking circle in which the second indicia rotatesaround the tracking circle with the roll movement of the C-arm.Alignment of the first and second indicia corresponds to alignment ofthe roll degree of freedom between the new and baseline images.

In many instances an x-ray image is taken at an angle to avoid certainanatomical structures or to provide the best image of a target. In theseinstances, the C-arm is canted or pitched to find the best orientationfor the baseline image. It is therefore desirable to match the new imageto the baseline image in six degrees of freedom—X and Y translations, Ztranslation corresponding to scaling (i.e., closer or farther away fromthe target), roll or rotation about the Z axis, and pitch and yaw(rotation about the X and Y axes, respectively). Aligning the viewfinder in the X, Y, Z and roll directions can be indicated by the colorof the tracking circle, as described above. It can be appreciated thatusing the view finder image appearing on the display four degrees offreedom of movement can be readily visualized, namely X and Ytranslation, zoom or Z translation and roll about the Z-axis. However,it is more difficult to directly visualize movement in the other twodegrees of freedom—pitch and yaw—on the image display. Aligning thetracking circle 240 in the pitch and yaw requires a bit more complicatedmovement of the C-arm and the view finder associated with the C-arm. Inorder to facilitate this movement and alignment, a vertical slider barcorresponding to the pitch movement and a horizontal slider barcorresponding to the yaw movement can be shown on the display. The newimage is properly located when indicators along the two slider bars arecentered. The slider bars can be in red when the new image is misalignedrelative to the baseline image in the pitch and yaw degrees of freedom,and can turn green when properly centered. Once all of the degrees offreedom have been aligned with the X, Y, Z, roll, pitch and yaworientations of the original baseline image, the technician can take thenew image and the surgeon can be assured that an accurate and meaningfulcomparison can be made between the new image and the baseline image.

The spatial position of the baseline image is known from the 6DOFposition information obtained when the baseline image was generated.This 6DOF position information includes the data from the trackingdevice 130 as well as any angular orientation information obtained fromthe C-arm itself. When it is desired to generate a new image at the samespatial position as the baseline image, new spatial position informationis being generated as the C-arm is moved. Whether the C-arm is alignedwith the baseline image position can be readily ascertained by comparingthe 6DOF position data, as described above. In addition, this comparisoncan be used to provide an indication to the radiology technician as tohow the C-arm needs to be moved to obtain proper alignment. In otherwords, if the comparison of baseline position data to current positiondata shows that the C-arm is misaligned to the left, an indication canbe provided directing the technician to move the C-arm to the right.This indication can be in the form of a direction arrow 242 that travelsaround the tracking circle 240, as depicted in the screen shot of FIG.8C. The direction of movement indicator 242 can be transformed to acoordinate system corresponding to the physical position of the C-armrelative to the technician. In other words, the movement indicator 242points vertically upward on the image in FIG. 8C to indicate that thetechnician needs to move the C-arm upward to align the current imagewith the baseline image. As an alternative to the direction arrow 242 onthe tracking circle, the movement direction may be indicated onperpendicular slider bars adjacent to the image, such as the bars 244,245 in FIG. 8C. The slider bars can provide a direct visual indicationto the technician of the offset of the bar from the centered position oneach bar. In the example of FIG. 8C the vertical slider bar 244 is belowthe centered position so the technician immediately knows to move theC-arm vertically upward.

In a further embodiment, two view finder images can be utilized by theradiology technician to orient the C-arm to acquire a new image at thesame orientation as a baseline image. In this embodiment, the two viewfinder images are orthogonal images, such as an anterior-posterior (AP)image (passing through the body from front to back) and a lateral (LAT)image (passing through the body shoulder to shoulder), as depicted inthe screen shot of FIG. 8D. The technician seeks to align both viewfinder images to corresponding AP and LAT baseline images. As the C-armis moved by the technician, both images are tracked simultaneously,similar to the single view finder described above. Each view finderincorporates a tracking circle which responds in the manner describedabove—i.e., red for out of bounds and green for in bounds. Thetechnician to switch between the AP and LAT viewfinders as the C-arm ismanipulated. Once the tracking circle is within a predetermined range ofproper alignment, the display can switch from the two view finderarrangement to the single view finder arrangement described above tohelp the technician to fine tune the position of the C-arm.

It can be appreciated that the two view navigation images may be derivedfrom a baseline image and a single shot or X-ray image at a currentposition, such as a single AP image. In this embodiment, the lateralimage is a projection of the AP image as if the C-arm was actuallyrotated to a position to obtain the lateral image. As the view finderfor the AP image is moved to position the view at a desired location,the second view finder image displays the projection of that image inthe orthogonal plane (i.e., the lateral view). The physician and x-raytechnician can thus maneuver the C-arm to the desired location for alateral view based on the projection of the original AP view. Once theC-arm is aligned with the desired location, the C-arm can then actuallybe positioned to obtain the orthogonal (i.e., lateral) x-ray image.

In the discussion above, the tracking function of the imaging systemdisclosed herein is used to return the C-arm to the spatial position atwhich the original baseline image was obtained. The technician canacquire a new image at the same location so that the surgeon can comparethe current image to the baseline image. Alternatively, this trackingfunction can be used by the radiology technician to acquire a new imageat a different orientation or at an offset location from the location ofa baseline image. For instance, if the baseline image was an AP view ofthe L3 vertebra and it is desired to obtain an image a specific featureof that vertebra, the tracking feature can be used to quickly guide thetechnician to the vertebra and then to the desired alignment over thefeature of interest. The tracking feature of the present invention thusallows the technician to find the proper position for the new imagewithout having to acquire intermediate images to verify the position ofthe C-arm relative to the desired view.

The image tracking feature can also be used when stitching multipleimages, such as to form a complete image of a patient's spine. Asindicated above, the tracking circle 240 depicts the location of theC-arm relative to the anatomy as if an image were taken at that locationand orientation. The baseline image (or some selected prior image) alsoappears on the display with the tracking circle offset from the baselineimage indicative of the offset of the C-arm from the position at whichthe displayed image was taken. The position of the tracking circlerelative to the displayed baseline image can thus be adjusted to providea degree of overlap between the baseline image and a new image taken atthe location of the tracking circle. Once a C-arm has been moved to adesired overlap, the new image can be taken. This new image is thendisplayed on the screen along with the baseline image as the two imagesare stitched together. The tracking circle is also visible on thedisplay and can be used to guide movement of the C-arm for another imageto be stitched to the other two images of the patient's anatomy. Thissequence can be continued until all of the desired anatomy has beenimaged and stitched together.

The present invention contemplates a feature that enhances thecommunication between the surgeon and the radiology technician. Duringthe course of a procedure the surgeon may request images at particularlocations or orientations. One example is what is known as a “Fergusonview” in spinal procedures in which an AP oriented C-arm is canted toalign directly over a vertebral end plate with the end plate oriented“flat” or essentially parallel with the beam axis of the C-arm.Obtaining a Ferguson view requires rotating the C-arm or the patienttable while obtaining multiple AP views of the spine, which iscumbersome and inaccurate using current techniques, requiring a numberof fluoroscopic images to be performed to find the one best aligned tothe endplate. The present invention allows the surgeon to overlay a gridonto a single image or stitched image and provide labels for anatomicfeatures that can then be used by the technician to orient the C-arm.Thus, as shown in FIG. 9A, the image processing device 122 is configuredto allow the surgeon to place a grid 245 within the tracking circle 240overlaid onto a Lateral image. The surgeon may also locate labels 250identifying anatomic structure, in this case spinal vertebrae. In thisparticular example, the goal is to align the L2-L3 disc space with thecenter grid line 246. To assist the technician, a trajectory arrow 255is overlaid onto the image to indicate the trajectory of an imageacquired with the C-arm in the current position. As the C-arm moves,changing orientation off of pure AP, the image processing deviceevaluates the C-arm position data obtained from the tracking device 230to determine the new orientation for trajectory arrow 255. Thetrajectory arrow thus moves with the C-arm so that when it is alignedwith the center grid line 246, as shown in FIG. 9B, the technician canshoot the image knowing that the C-arm is properly aligned to obtain aFerguson view along the L3 endplate. Thus, monitoring the lateral viewuntil it is rotated and centered along the center grid line allows theradiology technician to find the AP Ferguson angle without guessing andtaking a number of incorrect images.

The image processing device may be further configured to show thelateral and AP views simultaneously on respective displays 123 and 124,as depicted in FIG. 10. Either or both views may incorporate the grid,labels and trajectory arrows. This same lateral view may appear on thecontrol panel 110 for the imaging system 100 for viewing by thetechnician. As the C-arm is moved to align the trajectory arrow with thecenter grid line, as described above, both the lateral and AP images aremoved accordingly so that the surgeon has an immediate perception ofwhat the new image will look like. Again, once the technician properlyorients the C-arm, as indicated by alignment of the trajectory arrowwith the center grid line, a new AP image is acquired. As shown in FIG.10, a view may include multiple trajectory arrows, each aligned with aparticular disc space. For instance, the uppermost trajectory arrow isaligned with the L1-L2 disc space, while the lowermost arrow is alignedwith the L5-S1 disc space. In multiple level procedures the surgeon mayrequire a Ferguson view of different levels, which can be easilyobtained by requesting the technician to align the C-arm with aparticular trajectory arrow. The multiple trajectory arrows shown inFIG. 10 can be applied in a stitched image of a scoliotic spine and usedto determine the Cobb angle. Changes in the Cobb angle can be determinedlive or interactively as correction is applied to the spine. A currentstitched image of the corrected spine can be overlaid onto a baselineimage or switched between the current and baseline images to provide adirect visual indication of the effect of the correction.

In another feature, a radiodense asymmetric shape or glyph can be placedin a known location on the C-arm detector. This creates the ability tolink the coordinate frame of the C-arm to the arbitrary orientation ofthe C-arm's image coordinate frame. As the C-arm's display may bemodified to generate an image having any rotation or mirroring,detecting this shape radically simplifies the process of imagecomparison and image stitching. Thus, as shown in FIG. 11, the baselineimage B includes the indicia or glyph “K” at the 9 o'clock position ofthe image. In an alternative embodiment, the glyph may be in the form ofan array of radio-opaque beads embedded in a radio-transparent componentmounted to a C-arm collar, such as in a right triangular pattern. Sincethe physical orientation and location of the glyph relative to the C-armis fixed, knowing the location and orientation of the glyph in a 2Dimage provides an automatic indication of the orientation of the imagewith respect to the physical world. The new image N is obtained in whichthe glyph has been rotated by the physician or technologist away fromthe default orientation. Comparing this new image to the baseline imageset is unlikely to produce any registration between images due to thisangular offset. In one embodiment, the image processing device detectsthe actual rotation of the C-arm from the baseline orientation while inanother embodiment the image processing device uses image recognitionsoftware to locate the “K” glyph in the new image and determine theangular offset from the default position. This angular offset is used toalter the rotation and/or mirror image the baseline image set. Thebaseline image selected in the image registration step 210 is maintainedin its transformed orientation to be merged with the newly acquiredimage. This transformation can include rotation and mirror-imaging, toeliminate the display effect that is present on a C-arm. The rotationand mirroring can be easily verified by the orientation of the glyph inthe image. It is contemplated that the glyph, whether the “K” or theradio-opaque bead array, provides the physician with the ability tocontrol the way that the image is displayed for navigation independentof the way that the image appears on the X-ray screen used by thetechnician. In other words, the imaging and navigation system disclosedherein allows the physician to rotate, mirror or otherwise manipulatethe displayed image in a manner that physician wants to see whileperforming the procedure. The glyph provides a clear indication of themanner in which the image used by the physician has been manipulated inrelation to the X-ray image. Once the physician's desired orientation ofthe displayed image has been set, the ensuing images retain that sameorientation regardless of how the C-arm has been moved.

In another aspect, it is known that as the C-arm radiation source 104moves closer to the table, the size of the image captured by thereceiver 105 becomes larger; moving the receiver closer to the tableresults in a decrease in image size. Whereas the amount that the imagescales with movements towards and away from the body can be easilydetermined, if the C-arm is translated along the table, the image willshift, with the magnitude of that change depending upon the proximity ofthe “center of mass” (COM) of the patient to the radiation source.Although the imaged anatomy is of 3D structures, with a high degree ofaccuracy, mathematically we can represent this anatomy as a 2D pictureof the 3D anatomy placed at the COM of the structures. Then, forinstance, when the COM is close to the radiation source, small movementswill cause the resulting image to shift greatly. Until the COM isdetermined, though, the calculated amount that the objects on the screenshift will be proportional to but not equal to their actual movement.The difference is used to calculate the actual location of the COM. TheCOM is adjusted based on the amount that those differ, moving it awayfrom the radiation source when the image shifted too much, and theopposite if the image shifts too little. The COM is initially assumed tobe centered on the table to which the reference arc of the trackingdevice is attached. The true location of the COM is fairly accuratelydetermined using the initial two or three images taken during initialset-up of the imaging system, and reconfirmed/adjusted with each newimage taken. Once the COM is determined in global space, the movement ofthe C-arm relative to the COM can be calculated and applied to translatethe baseline image set accordingly for image registration.

The image processing device 122 may also be configured to allow thesurgeon to introduce other tracked elements into an image, to help guidethe surgeon during the procedure. A closed-loop feedback approach allowsthe surgeon to confirm that the location of this perceived trackedelement and the image taken of that element correspond. Specifically,the live x-ray and the determined position from the surgical navigationsystem are compared. In the same fashion that knowledge of the baselineimage, through image recognition, can be used to track the patient'sanatomy even if blocked by radiodense objects, knowledge of theradiodense objects, when the image taken is compared to their trackedlocation, can be used to confirm their tracking. When both theinstrument/implant and the C-arm are tracked, the location of theanatomy relative to the imaging source and the location of the equipmentrelative to the imaging source are known. This information can thus beused to quickly and interactively ascertain the location of theequipment or hardware relative to the anatomy. This feature can, by wayof example, have particular applicability to following the path of acatheter in an angio procedure, for instance. In a typical angioprocedure, a cine, or continuous fluoro, is used to follow the travel ofthe catheter along a vessel. The present invention allows intersplicingpreviously generated images of the anatomy with the virtual depiction ofthe catheter with live fluoro shots of the anatomy and actual catheter.Thus, rather than taking 15 fluoro shots per second for a typical cineprocedure, the present invention allows the radiology technician to takeonly one shot per second to effectively and accurately track thecatheter as it travels along the vessel. The previously generated imagesare spliced in to account for the fluoro shots that are not taken. Thevirtual representations can be verified to the live shot when taken andrecalibrated if necessary.

This same capability can be used to track instrumentation inimage-guided or robotic surgeries. When the instrumentation is trackedusing conventional tracking techniques, such as EM tracking, thelocation of the instrumentation in space is known. The imaging systemdescribed herein provides the location of the patient's imaged anatomyin space, so the present system knows the relative location of theinstrument to that anatomy. However, it is known that distortion of EMsignals occurs in a surgical and C-arm environment and that thisdistortion can distort the location of the instrument in the image. Whenthe position of the instrument in space is known, by way of the trackingdata, and the 2D plane of the x-ray image is known, as obtained by thepresent system, then the projection of the instrument onto that 2D planecan be readily determined. The imaged location of the instrument canthen be corrected in the final image to eliminate the effects ofdistortion. In other words, if the location and position of theinstrument is known from the tracking data and 3D model, then thelocation and position of the instrument on the 2D image can becorrected. One approach to correcting for distortion is described in theAppendix, the entire disclosure of which is incorporated herein byreference.

In certain procedures it is possible to fix the position of the vascularanatomy to larger features, such as nearby bones. This can beaccomplished using DRRs from prior CT angiograms (CTA) or from actualangiograms taken in the course of the procedure. Either, approach may beused as a means to link angiograms back to bony anatomy and vice versa.To describe in greater detail, the same CTA may be used to producedifferent DRRs, such as DRRs highlighting just the bony anatomy andanother in a matched set that includes the vascular anatomy along withthe bones. A baseline fluoro image taken of the patient's bony anatomycan then be compared with the bone DRRs to determine the best match.Instead of displaying the result using bone only DRR, the matched DRRthat includes the vascular anatomy can be used to merge with the newimage. In this approach, the bones help to place the radiographicposition of the catheter to its location within the vascular anatomy.Since it is not necessary to continually image the vessel itself, as thepicture of this structure can be overlaid onto the bone only imageobtained, the use of contrast dye can be limited versus prior proceduresin which the contrast dye is necessary to constantly see the vessels.

Details of one approach to tracking the C-arm and instrumentation foruse in the imaging and tracking functions of the present system aredescribed in the Appendix, the entire description of which isincorporated herein by reference.

Following are examples of specific procedures utilizing the features ofthe image processing device discussed above. These are just a fewexamples as to how the software can be manipulated using differentcombinations of baseline image types, display options, and radiationdosing and not meant to be an exhaustive list.

Pulsed New Image/Alternated with/Baseline of FD Fluoro or preoperativeX-ray

A pulsed image is taken and compared with a previously obtained baselineimage set containing higher resolution non-pulsed image(s) taken priorto the surgical procedure. Registration between the current image andone of the baseline solution set provides a baseline image reflectingthe current position and view of the anatomy. The new image isalternately displayed or overlaid with the registered baseline image,showing the current information overlaid and alternating with the lessobscured or clearer image.

Pulsed New Image/Alternated with/Baseline derived from DRR

A pulsed image is taken and compared with a previously obtained solutionset of baseline images, containing higher resolution DRR obtained from aCT scan. The DRR image can be limited to just show the bony anatomy, asopposed to the other obscuring information that frequently “cloud” afilm taken in the OR (e.g.—bovie cords, EKG leads, etc.) as well asobjects that obscure bony clarity (e.g.—bowel gas, organs, etc.). Aswith the above example, the new image that is registered with one of theprior DRR images, and these images are alternated or overlaid on thedisplay 123, 124.

Pulsed New Image/Merged Instead of Alternated

All of the techniques described above can be applied and instead ofalternating the new and registered baseline images, the prior andcurrent image are merged. By performing a weighted average or similarmerging technique, a single image can be obtained which shows both thecurrent information (e.g.—placement of instruments, implants, catheters,etc.) in reference to the anatomy, merged with a higher resolutionpicture of the anatomy. In one example, multiple views of the merger ofthe two images can be provided, ranging from 100% pulsed image to 100%DRR image. A slide button on the user interface 125 allows the surgeonto adjust this merger range as desired.

New Image is a Small Segment of a Larger Baseline Image Set

The imaging taken at any given time contains limited information, a partof the whole body part. Collimation, for example, lowers the overalltissue radiation exposure and lowers the radiation scatter towardsphysicians but at the cost of limiting the field of view of the imageobtained. Showing the actual last projected image within the context ofa larger image (e.g.—obtained prior, preoperatively or intraoperatively,or derived from CTs)—merged or alternated in the correction location—cansupplement the information about the smaller image area to allow forincorporation into reference to the larger body structure(s). The sameimage registration techniques are applied as described above, exceptthat the registration is applied to a smaller field within the baselineimages (stitched or not) corresponding to the area of view in the newimage.

Same as Above, Located at Junctional or Blocked Areas

Not infrequently, especially in areas that have different overalldensities (e.g.—chest vs. adjacent abdomen, head/neck/cervical spine vs.upper thorax), the area of an x-ray that can be clearly visualized isonly part of the actual image obtained. This can be frustrating to thephysician when it limits the ability to place the narrow view into thelarger context of the body or when the area that needs to be evaluatedis in the obscured part of the image. By stitching together multipleimages, each taken in a localized ideal environment, a larger image canbe obtained. Further, the current image can be added into the largercontext (as described above) to fill in the part of the image clouded byits relative location.

Unblocking the Hidden Anatomy or Mitigating its Local Effects

As described above, the image processing device performs the imageregistration steps between the current new image and a baseline imageset that, in effect, limits the misinformation imparted by noise, be itin the form of x-ray scatter or small blocking objects (e.g.—cords,etc.) or even larger objects (e.g.—tools, instrumentation, etc.). Inmany cases, it is that part of the anatomic image that is being blockedby a tool or instrument that is of upmost importance to the surgerybeing performed. By eliminating the blocking objects from the image thesurgery becomes safer and more efficacious and the physician becomesempowered to continue with improved knowledge. Using an image that istaken prior to the noise being added (e.g.—old films, baseline single FDimages, stitched together fluoro shots taken prior to surgery, etc.) oridealized (e.g. DRRs generated from CT data), displaying that prior“clean” image, either merged or alternated with the current image, willmake those objects disappear from the image or become shadows ratherthan dense objects. If these are tracked objects, then the blocked areacan be further deemphasized or the information from it can be eliminatedas the mathematical comparison is being performed, further improving thespeed and accuracy of the comparison.

The image processing device configured as described herein providesthree general features that (1) reduce the amount of radiation exposurerequired for acceptable live images, (2) provide images to the surgeonthat can facilitate the surgical procedure, and (3) improve thecommunication between the radiology technician and the surgeon. Withrespect to the aspect of reducing the radiation exposure, the presentinvention permits low dose images to be taken throughout the surgicalprocedure and fills in the gaps created by “noise” in the current imageto produce a composite or merged image of the current field of view withthe detail of a full dose image. In practice this allows for highlyusable, high quality images of the patient's anatomy generated with anorder of magnitude reduction in radiation exposure than standard FDimaging using unmodified features present on all common, commerciallyavailable C-arms. The techniques for image registration described hereincan be implemented in a graphic processing unit and can occur in asecond or so to be truly interactive; when required such as in CINEmode, image registration can occur multiple times per second. A userinterface allows the surgeon to determine the level of confidencerequired for acquiring registered image and gives the surgeon options onthe nature of the display, ranging from side-by-side views to fadein/out merged views.

With respect to the feature of providing images to the surgeon thatfacilitate the surgical procedure, several digital imaging techniquescan be used to improve the user's experience. One example is an imagetracking feature that can be used to maintain the image displayed to thesurgeon in an essentially a “stationary” position regardless of anyposition changes that may occur between image captures. In accordancewith this feature, the baseline image can be fixed in space and newimages adjust to it rather than the converse. When successive images aretaken during a step in a procedure each new image can be stabilizedrelative to the prior images so that the particular object of interest(e.g.—anatomy or instrument) is kept stationary in successive views. Forexample, as sequential images are taken as a bone screw is introducedinto a body part, the body part remains stationary on the display screenso that the actual progress of the screw can be directly observed.

In another aspect of this feature, the current image including blockingobjects can be compared to earlier images without any blocking objects.In the registration process, the image processing device can generate amerged image between new image and baseline image that deemphasizes theblocking nature of the object from the displayed image. The userinterface also provides the physician with the capability to fade theblocking object in and out of the displayed view.

In other embodiments in which the object itself is being tracked, avirtual version of the blocking object can be added back to thedisplayed image. The image processing device can obtain position datafrom a tracking device following the position of the blocking object anduse that position data to determine the proper location and orientationof the virtual object in the displayed image. The virtual object may beapplied to a baseline image to be compared with a new current image toserve as a check step—if the new image matches the generated image (bothtool and anatomy) within a given tolerance then the surgery can proceed.If the match is poor, the surgery can be stopped (in the case ofautomated surgery) and/or recalibration can take place. This allows fora closed-loop feedback feature to facilitate the safety of automation ofmedical intervention.

For certain procedures, such as a pseudo-angio procedure, projecting thevessels from a baseline image onto current image can allow a physicianto watch a tool (e.g.—micro-catheter, stent, etc.) as it travels throughthe vasculature while using much less contrast medium load. The adjacentbony anatomy serves as the “anchor” for the vessels—the bone isessentially tracked, through the image registration process, and thevessel is assumed to stay adjacent to this structure. In other words,when the anatomy moves between successive images, the new image isregistered to a different one of the baseline image set that correspondsto the new position of the “background” anatomy. The vessels from adifferent but already linked baseline image containing the vascularstructures can then be overlaid or merged with the displayed image whichlacks contrast. If necessary or desired, intermittent angios can betaken to confirm. When combined with a tracked catheter, a workingknowledge of the location of the instrument can be included into theimages. A cine (continuous movie loop of fluoro shots commonly used whenan angiogram is obtained) can be created in which generated images areinterspliced into the cine images, allowing for many fewer x-rays to beobtained while an angiogram is being performed or a catheter is beingplaced. Ultimately, once images have been linked to the originalbaseline image, any of these may be used to merge into a current image,producing a means to monitor movement of implants, the formation ofconstructs, the placement of stents, etc.

In the third feature—improving communication—the image processing devicedescribed herein allows the surgeon to annotate an image in a mannerthat can help guide the technician in the positioning of the C-arm as tohow and where to take a new picture. Thus, the user interface 125 of theimage processing device 122 provides a vehicle for the surgeon to add agrid to the displayed image, label anatomic structures and/or identifytrajectories for alignment of the imaging device. As the technicianmoves the imaging device or C-arm, the displayed image is moved. Thisfeature allows the radiology tech to center the anatomy that is desiredto be imaged in the center of the screen, at the desired orientation,without taking multiple images each time the C-arm is brought back inthe field to obtain this. This feature provides a view finder for theC-arm, a feature lacking currently. The technician can activate theC-arm to take a new image with a view tailored to meet the surgeon'sexpressed need.

In addition, linking the movements of the C-arm to the images takenusing DICOM data or a surgical navigation backbone, for example, helpsto move the displayed image as the C-arm is moved in preparation for asubsequent image acquisition. “In bound” and “out of bounds” indicatorscan provide an immediate indication to the technician whether a currentmovement of the C-arm would result in an image that cannot be correlatedor registered with any baseline image, or that cannot be stitchedtogether with other images to form a composite field of view. The imageprocessing device thus provides image displays that allow the surgeonand technician to visualize the effect of a proposed change in locationand trajectory of the c-arm. Moreover, the image processing device mayhelp the physician, for instance, alter the position of the table or theangle of the C-arm so that the anatomy is aligned properly (such asparallel or perpendicular to the surgical table). The image processingdevice can also determine the center of mass (COM) of the exact centerof an x-rayed object using two or more x-ray shots from two or moredifferent gantry angles/positions, and then use this COM information toimprove the linking of the physical space (in millimeters) to thedisplayed imaging space (in pixels).

The image recognition component disclosed herein can overcome the lackof knowledge of the location of the next image to be taken, whichprovides a number of benefits. Knowing roughly where the new image iscentered relative to the baseline can limit the need to scan a largerarea of the imaging space and, therefore, significantly increase thespeed of image recognition software. Greater amounts of radiationreduction (and therefore noise) can be tolerated, as there exists aninternal check on the image recognition. Multiple features that aremanual in the system designed without surgical navigation, such asbaseline image creation, switching between multiple baseline image sets,and stitching, can be automated. These features are equally useful in animage tracking context.

As described above, the systems and methods correlate or synchronize thepreviously obtained images with the live images to ensure that anaccurate view of the surgical site, anatomy and hardware, is presentedto the surgeon. In an optimum case, the previously obtained images arefrom the particular patient and are obtained near in time to thesurgical procedure. However, in some cases no such prior image isavailable. In such cases, the “previously obtained image” can beextracted from a database of CT and DRR images. The anatomy of mostpatients is relatively uniform depending on the height and stature ofthe patient. From a large database of images there is a high likelihoodthat a prior image or images of a patient having substantially similaranatomy can be obtained. The image or images can be correlated to thecurrent imaging device location and view, via software implemented bythe image processing device 122, to determine if the prior image issufficiently close to the anatomy of the present patient to reliablyserve as the “previously obtained image” to be interspliced with thelive images.

The display in FIG. 10 is indicative of the type of display and userinterface that may be incorporated into the image processing device 122,user interface 125 and display device 126. For instance, the displaydevice may include the two displays 122, 123 with “radio” buttons oricons around the perimeter of the display. The icons may be touch screenbuttons to activate the particular feature, such as the “label”, “grid”and “trajectory” features shown in the display. Activating a touchscreen or radio button can access a different screen or pull down menuthat can be used by the surgeon to conduct the particular activity. Forinstance, activating the “label” button may access a pull down menu withthe labels “L1”, “L2”, etc., and a drag and drop feature that allows thesurgeon to place the labels at a desire location on the image. The sameprocess may be used for placing the grid and trajectory arrows shown inFIG. 10.

The same system and techniques described above may be implemented wherea collimator is used to reduce the field of exposure of the patient. Forinstance, as shown in FIG. 12A, a collimator may be used to limit thefield of exposure to the area 300 which presumably contains the criticalanatomy to be visualized by the surgeon or medical personnel. As isapparent from FIG. 12A the collimator prevents viewing the region 301that is covered by the plates of the collimator. Using the system andmethods described above, prior images of the area 315 outside thecollimated area 300 are not visible to the surgeon in the expanded fieldof view 310 provided by the present system.

The same principles may be applied for images obtained using a movingcollimator. As depicted in the sequence of FIGS. 13A, 14A, 15A and 16Athe visible field is gradually shifted to the left in the figures as themedical personnel zeroes in on a particular part of the anatomy. Usingthe system and methods described herein, the image available to themedial personnel is shown in FIGS. 13B, 14B, 15B and 16B in which theentire local anatomy is visible. It should be understood that only thecollimated region (i.e. region 300 in FIG. 12A) is a real-time image.The image outside the collimated region is obtained from previous imagesas described above. Thus, the patient is still subject to a reduceddosage of radiation while the medical personnel is provided with acomplete view of the relevant anatomy. As described above, the currentimage can be merged with the baseline or prior image, can be alternatedor even displayed un-enhanced by imaging techniques described herein.

The present disclosure contemplates a system and method in whichinformation that would otherwise be lost because it is blocked by acollimator, is made available to the surgeon or medical personnelinteractively during the procedure. Moreover, the systems and methodsdescribed herein can be used to limit the radiation applied in thenon-collimated region. These techniques can be applied whether theimaging system or collimator are held stationary or are moving.

In a further aspect, the systems and methods described herein may beincorporated into an image-based approach for controlling the state of acollimator in order to reduce patient exposure to X-rays during surgicalprocedures that require multiple X-ray images of the same anatomicalregion. In particular, the boundaries of the aperture of the collimatorare determined by the location of the anatomical features of interest inpreviously acquired images. Those parts of the image that are notimportant to the surgical procedure can be blocked by the collimator,but then filled in with the corresponding information from thepreviously acquired images, using the systems and methods describedabove and in published application US-2012-0087562-A1. The collimatedimage and the previous images can be displayed on the screen in a singlemerged view, they can be alternated, or the collimated image can beoverlaid on the previous image. To properly align the collimated imagewith the previous image, image-based registration similar to thatdescribed in published application US-2012-0087562-A1 can be employed.

In one approach, the anatomical features of interest can be determinedmanually by the user drawing a region of interest on a baseline orpreviously obtained image. In another approach, an object of interest inthe image is identified, and the collimation follows the object as itmoves through the image. When the geometric state of the X-ray system isknown, the movement of the features of interest in the detector field ofview can be tracked while the system moves with respect to the patient,and the collimator aperture can be adjusted accordingly, as illustratedin FIGS. 13A, 13B, 14A, 14B, 15A, 15B, 16A, 16B. The geometric state ofthe system can be determined with a variety of methods, includingoptical tracking, electromagnetic tracking, and accelerometers.

In another aspect of the present disclosure, the systems and methodsdescribed herein and in published application US-2012-0087562-A1 can beemployed to control radiation dosage. An X-ray tube consists of a vacuumtube with a cathode and an anode at opposite ends. When an electriccurrent is supplied to the cathode, and a voltage is applied across thetube, a beam of electrons travels from the cathode to the anode andstrikes a metal target. The collisions of the electrons with the metalatoms in the target produce X-rays, which are emitted from the tube andused for imaging. The strength of the emitted radiation is determined bythe current, voltage, and duration of the pulses of the beam ofelectrons. In most medical imaging systems, such as C-arms, theseparameters are controlled by an automatic exposure control (AEC) system.This system uses a brief initial pulse in order to generate a testimage, which can be used to subsequently optimize the parameters formaximizing image clarity while minimizing radiation dosage.

One problem with existing AEC systems is that they do not account forthe ability of image processing software to exploit the persistence ofanatomical features in medical images in order to achieve furtherimprovements in image clarity and reductions in radiation dosage. Thistechniques described herein utilize software and hardware elements tocontinuously receive the images produced by the imaging system andrefine these images by combining them with images acquired at previoustimes. The software elements also compute an image quality metric andestimates how much the radiation exposure can be increased or decreasedfor the metric to achieve a certain ideal value. This value isdetermined by studies of physician evaluations of libraries of medicalimages acquired at various exposure settings, and may be provided in atable look-up stored in a system memory accessible by the softwareelements, for example. The software converts the estimated changes tothe amounts of emitted radiation into exact values for the voltage andcurrent to be applied to the X-ray tube. The hardware element consistsof an interface from the computer running the image processing softwareto the controls of the X-ray tube that bypasses the AEC and sets thevoltage and current.

While the invention has been illustrated and described in detail in thedrawings and foregoing description, the same should be considered asillustrative and not restrictive in character. It is understood thatonly the preferred embodiments have been presented and that all changes,modifications and further applications that come within the spirit ofthe invention are desired to be protected.

What is claimed is:
 1. A method for generating a display of an image ofa patient's internal anatomy in a surgical field during a medicalprocedure, comprising: with one or more processors: acquiring a highresolution baseline image of the surgical field including the patient'sinternal anatomy in a baseline orientation, wherein the high resolutionbaseline image is a digitally reconstructed radiograph from a computedtomography scan image; digitally manipulating the high resolutionbaseline image to produce a baseline image set including representativeimages of the baseline image at a plurality of permutations of movementsof the baseline image from the baseline orientation, wherein thepermutations of movements includes movements in six degrees of freedomcorresponding to a three-dimensional image; acquiring a new image of thesurgical field at a lower resolution; comparing the new image to therepresentative images in the baseline image set and selecting therepresentative image having an acceptable degree of correlation with thenew image; and merging the selected representative image with the newimage and displaying the merged image.
 2. The method of claim 1, whereinthe new image is one of a pulse or low dose image.
 3. The method ofclaim 1, wherein: in the step of digitally manipulating the highresolution image the permutations of movements form a predefined grid ofimage movements; and the step of comparing the new image to therepresentative images of the baseline image set includes comparingoverlapping pixels between the representative image and the new image.4. The method of claim 1, wherein the step of comparing the new image tothe representative images of the baseline image set includesheuristically selecting representative images for comparison.
 5. Themethod of claim 1, wherein the step of comparing the new image to therepresentative images of the baseline image set includes: performing aprincipal component analysis (PCA) on the pixels of the representativeimages in the baseline image set to generate one or more PCA vectors;producing a PCA matrix of PCA vectors for each pixel in a representativeimage; generating a column vector for each representative image and thenew image of pixel data for each pixel in the image; performing a matrixmultiplication of the PCA matrix and each column vector to generate anew column vector for each representative image and the new image;obtaining the dot product of the column vector for the new image and thecolumn vector for each of the representative image; and selecting arepresentative image for which the dot product is within apre-determined threshold.
 6. The method of claim 1, in which the medicalprocedure includes tools, instruments, implants or other objects thatblock or obscure the internal anatomy in an image of the surgical field,wherein the step of comparing the new image to the representative imagesof the baseline image set includes only comparing portions of the imagesoutside the portions that are blocked or obscured.
 7. The method ofclaim 6, wherein the location of the blocked or obscured portions of thenew image are determined by determining which pixels have a valueoutside a pre-determined threshold.
 8. The method of claim 1, wherein:the step of digitally manipulating the high resolution baseline imageincludes providing parallel images to each representative image in whichcertain anatomic features are reduced or enhanced; and the step ofmerging the selected representative image includes merging anddisplaying the parallel image to the selected representative image. 9.An image processing apparatus for generating a display of an image of apatient's internal anatomy during a medical procedure, comprising: amemory for storing a high resolution baseline image of the surgicalfield including the patient's internal anatomy in a baseline orientationand a new image of the surgical field at a low resolution, wherein thehigh resolution baseline image is a digitally reconstructed radiographfrom a computed tomography scan image; and a processor configured to:digitally manipulate the high resolution baseline image to produce abaseline image set including representative images of the baseline imageat a plurality of permutations of movements of the baseline image fromthe baseline orientation, wherein the permutations of movements includesmovements in six degrees of freedom corresponding to a three-dimensionalimage; perform software instructions for comparing the new image to therepresentative images in the baseline image set and selecting therepresentative image having an acceptable degree of correlation with thenew image; digitally merging the selected representative image with thenew image; and generating signals for displaying the merged image on adisplay device.
 10. The image processing apparatus of claim 9, whereinthe processor is configured to digitally manipulating the highresolution image such that the permutations of movements form apredefined grid of image movements; and the software instructions forcomparing the new image to the representative images of the baselineimage set includes comparing overlapping pixels between therepresentative image and the new image.
 11. The image processingapparatus of claim 9, wherein the software instructions for comparingthe new image to the representative images of the baseline image setincludes: performing a principal component analysis (PCA) on the pixelsof the representative images in the baseline image set to generate oneor more PCA vectors; producing a PCA matrix of PCA vectors for eachpixel in a representative image; generating a column vector for eachrepresentative image and the new image of pixel data for each pixel inthe image; performing a matrix multiplication of the PCA matrix and eachcolumn vector to generate a new column vector for each representativeimage and the new image; obtaining the dot product of the column vectorfor the new image and the column vector for each of the representativeimage; and selecting a representative image for which the dot product iswithin a pre-determined threshold.
 12. The image processing apparatus ofclaim 9, in which the medical procedure includes tools, instruments,implants or other objects that block or obscure the internal anatomy inan image of the surgical field, wherein the software instructions forcomparing the new image to the representative images of the baselineimage set includes only comparing portions of the images outside theportions that are blocked or obscured.
 13. The image processingapparatus of claim 12, wherein the location of the blocked or obscuredportions of the new image are determined by determining which pixelshave a value outside a pre-determined threshold.
 14. The imageprocessing apparatus of claim 9, further comprising a user interfaceoperable to allow manual adjustment of the degree of digitally mergingthe selected representative image with the new image.
 15. The imageprocessing apparatus of claim 9, wherein: the user interface is furtheroperable to allow manually switching between a display of one or more ofthe representative image, the new image and the merged image; and theprocessor generates signals for displaying on a display device accordingto the user interface.